introductory
RAPTOR (Recursive Abstract Processing for Tree-Structured Retrieval Enhanced Generation) is an advanced Retrieval Enhanced Generation (RAG) method. It enhances traditional document structuring and summarization techniques by introducing a hierarchy of RAG Process.
https://github.com/adithya-s-k/AI-Engineering.academy/tree/main/RAG/09_RAPTOR
locomotive
Traditional RAG systems often struggle with large document collections and complex queries. raptor addresses these challenges by creating a hierarchical representation of the document corpus, enabling more detailed and efficient retrieval.
Methodological details
Document preprocessing and vector store creation
- Break documents into manageable chunks.
- Embed each nugget using the appropriate embedding model.
- Clustering of embedding vectors to group similar content.
- The clustering results are summarized to create a higher level abstract representation.
- Use these summaries and original text blocks to construct a hierarchical tree structure (RAPTOR tree).
Retrieval Enhancement Generation Workflow
- User queries are embedded using the same embedding model.
- Traverses the RAPTOR tree to find related nodes (summaries or document blocks).
- Merge the search results with the original user query to form a context.
- Pass this context to the Large Language Model (LLM) to generate the final response.
Core features of RAPTOR
- Hierarchical Document Representation: Creates a tree structure of document content.
- Multi-level summaries: summarized information is provided at different levels.
- Efficient retrieval: faster and more relevant information retrieval through tree traversal.
- Scalability: allows better handling of large document collections than flat vector storage.
Advantages of this method
- Improved contextual relevance: Hierarchical structure better matches queries with relevant content.
- Forest search is more efficient: the tree traversal approach is more efficient compared to a full search.
- Handling Complex Queries: The multi-level structure helps to handle queries for information across multiple document sections.
- Handles large document sets: better scalability than traditional methods.
reach a verdict
RAPTOR enhances the quality and efficiency of the RAG process by introducing summarization and tree-structured document representation and retrieval mechanisms. This approach is expected to significantly improve the accuracy and contextual relevance of information retrieval, especially for large-scale complex document collections.
